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  <section id="beyondml-tflow-layers-package">
<h1>beyondml.tflow.layers package<a class="headerlink" href="#beyondml-tflow-layers-package" title="Permalink to this heading"></a></h1>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
</section>
<section id="module-beyondml.tflow.layers.FilterLayer">
<span id="beyondml-tflow-layers-filterlayer-module"></span><h2>beyondml.tflow.layers.FilterLayer module<a class="headerlink" href="#module-beyondml.tflow.layers.FilterLayer" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.FilterLayer.FilterLayer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.FilterLayer.</span></span><span class="sig-name descname"><span class="pre">FilterLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/FilterLayer.html#FilterLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.FilterLayer.FilterLayer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Layer which filters inputs based on status of <cite>on</cite> or <cite>off</cite></p>
<p>Example:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Create a model with just a FilterLayer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">filter_layer</span> <span class="o">=</span> <span class="n">mann</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">FilterLayer</span><span class="p">()(</span><span class="n">input_layer</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">input_layer</span><span class="p">,</span> <span class="n">filter_layer</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Call the model with the layer turned on</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="go">array([[0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]], dtype=float32)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Turn off the FilterLayer and call it again</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">turn_off</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Model must be recompiled after turning the layer on or off</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="go">array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.FilterLayer.FilterLayer.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/FilterLayer.html#FilterLayer.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.FilterLayer.FilterLayer.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.FilterLayer.FilterLayer.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/FilterLayer.html#FilterLayer.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.FilterLayer.FilterLayer.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.FilterLayer.FilterLayer.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/FilterLayer.html#FilterLayer.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.FilterLayer.FilterLayer.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.FilterLayer.FilterLayer.turn_off">
<span class="sig-name descname"><span class="pre">turn_off</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/FilterLayer.html#FilterLayer.turn_off"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.FilterLayer.FilterLayer.turn_off" title="Permalink to this definition"></a></dt>
<dd><p>Turn the layer <cite>off</cite> so inputs are destroyed and all-zero tensors are output</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.FilterLayer.FilterLayer.turn_on">
<span class="sig-name descname"><span class="pre">turn_on</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/FilterLayer.html#FilterLayer.turn_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.FilterLayer.FilterLayer.turn_on" title="Permalink to this definition"></a></dt>
<dd><p>Turn the layer <cite>on</cite> so inputs are returned unchanged as outputs</p>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MaskedConv2D">
<span id="beyondml-tflow-layers-maskedconv2d-module"></span><h2>beyondml.tflow.layers.MaskedConv2D module<a class="headerlink" href="#module-beyondml.tflow.layers.MaskedConv2D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv2D.MaskedConv2D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MaskedConv2D.</span></span><span class="sig-name descname"><span class="pre">MaskedConv2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv2D.html#MaskedConv2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv2D.MaskedConv2D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Masked 2-dimensional convolutional layer. For full documentation of the convolutional architecture, see the
TensorFlow Keras Convolutional2D layer documentation.</p>
<p>This layer implements masking consistent with the BeyondML API to support developing sparse models.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv2D.html#MaskedConv2D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv2D.html#MaskedConv2D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv2D.html#MaskedConv2D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv2D.html#MaskedConv2D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.kernel_size">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel_size</span></span><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.kernel_size" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.set_masks">
<span class="sig-name descname"><span class="pre">set_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_masks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv2D.html#MaskedConv2D.set_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv2D.MaskedConv2D.set_masks" title="Permalink to this definition"></a></dt>
<dd><p>Set the masks for the layer</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>new_masks</strong> (<em>list</em><em> of </em><em>arrays</em><em> or </em><em>array-likes</em>) – The new masks to set for the layer</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MaskedConv3D">
<span id="beyondml-tflow-layers-maskedconv3d-module"></span><h2>beyondml.tflow.layers.MaskedConv3D module<a class="headerlink" href="#module-beyondml.tflow.layers.MaskedConv3D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv3D.MaskedConv3D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MaskedConv3D.</span></span><span class="sig-name descname"><span class="pre">MaskedConv3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv3D.html#MaskedConv3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv3D.MaskedConv3D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Masked 3-dimensional convolutional layer. For full documentation of the
convolutional architecture, see the TensorFlow Keras Convolutional3D layer documentation.</p>
<p>This layer implements masking consistent with the BeyondML API to support
developing sparse models</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv3D.html#MaskedConv3D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv3D.html#MaskedConv3D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv3D.html#MaskedConv3D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv3D.html#MaskedConv3D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.kernel_size">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel_size</span></span><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.kernel_size" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.set_masks">
<span class="sig-name descname"><span class="pre">set_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_masks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedConv3D.html#MaskedConv3D.set_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedConv3D.MaskedConv3D.set_masks" title="Permalink to this definition"></a></dt>
<dd><p>Set the masks for the layer</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>new_masks</strong> (<em>list</em><em> of </em><em>arrays</em><em> or </em><em>array-likes</em>) – The new masks to set for the layer</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MaskedDense">
<span id="beyondml-tflow-layers-maskeddense-module"></span><h2>beyondml.tflow.layers.MaskedDense module<a class="headerlink" href="#module-beyondml.tflow.layers.MaskedDense" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedDense.MaskedDense">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MaskedDense.</span></span><span class="sig-name descname"><span class="pre">MaskedDense</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedDense.html#MaskedDense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedDense.MaskedDense" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Masked fully connected layer. For full documentation of the fully-connected architecture, see the
TensorFlow Keras Dense layer documentation.</p>
<p>This layer implements masking consistent with the BeyondML API to support developing sparse models.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedDense.MaskedDense.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedDense.html#MaskedDense.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedDense.MaskedDense.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedDense.MaskedDense.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedDense.html#MaskedDense.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedDense.MaskedDense.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedDense.MaskedDense.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedDense.html#MaskedDense.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedDense.MaskedDense.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedDense.MaskedDense.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedDense.html#MaskedDense.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedDense.MaskedDense.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MaskedDense.MaskedDense.set_masks">
<span class="sig-name descname"><span class="pre">set_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_masks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MaskedDense.html#MaskedDense.set_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MaskedDense.MaskedDense.set_masks" title="Permalink to this definition"></a></dt>
<dd><p>Set the masks for the layer</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>new_masks</strong> (<em>list</em><em> of </em><em>arrays</em><em> or </em><em>array-likes</em>) – The new masks to set for the layer</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiConv2D">
<span id="beyondml-tflow-layers-multiconv2d-module"></span><h2>beyondml.tflow.layers.MultiConv2D module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiConv2D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv2D.MultiConv2D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiConv2D.</span></span><span class="sig-name descname"><span class="pre">MultiConv2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv2D.html#MultiConv2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv2D.MultiConv2D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Multitask 2-dimensional convolutional layer</p>
<p>This layer implements multiple stacks of convolutional weights to account for different ways individual
neurons activate for various tasks. It is expected that to train using the RSN2 algorithm that MultiMaskedConv2D
layers be used during training and then those layers be converted to this layer type.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv2D.MultiConv2D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv2D.html#MultiConv2D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv2D.MultiConv2D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv2D.MultiConv2D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv2D.html#MultiConv2D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv2D.MultiConv2D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv2D.MultiConv2D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv2D.html#MultiConv2D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv2D.MultiConv2D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv2D.MultiConv2D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv2D.html#MultiConv2D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv2D.MultiConv2D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv2D.MultiConv2D.kernel_size">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel_size</span></span><a class="headerlink" href="#beyondml.tflow.layers.MultiConv2D.MultiConv2D.kernel_size" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiConv3D">
<span id="beyondml-tflow-layers-multiconv3d-module"></span><h2>beyondml.tflow.layers.MultiConv3D module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiConv3D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv3D.MultiConv3D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiConv3D.</span></span><span class="sig-name descname"><span class="pre">MultiConv3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv3D.html#MultiConv3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv3D.MultiConv3D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Multitask 3-dimensional convolutional layer</p>
<p>This layer implements multiple stacks of convolutional weights to account for different ways individual
neurons activate for various tasks. It is expected that to train using the RSN2 algorithm that MultiMaskedConv3D
layers be used during training and then those layers be converted to this layer type.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv3D.MultiConv3D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv3D.html#MultiConv3D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv3D.MultiConv3D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv3D.MultiConv3D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv3D.html#MultiConv3D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv3D.MultiConv3D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv3D.MultiConv3D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv3D.html#MultiConv3D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv3D.MultiConv3D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv3D.MultiConv3D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiConv3D.html#MultiConv3D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiConv3D.MultiConv3D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiConv3D.MultiConv3D.kernel_size">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel_size</span></span><a class="headerlink" href="#beyondml.tflow.layers.MultiConv3D.MultiConv3D.kernel_size" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiDense">
<span id="beyondml-tflow-layers-multidense-module"></span><h2>beyondml.tflow.layers.MultiDense module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiDense" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiDense.MultiDense">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiDense.</span></span><span class="sig-name descname"><span class="pre">MultiDense</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiDense.html#MultiDense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiDense.MultiDense" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Multitask fully connected layer</p>
<p>This layer implements multiple stacks of fully connected weights to account for different
ways neurons can activate for various tasks. It is expected that to train using the RSN2 algorithm
that MultiMaskedDense layers be used during training and then those layers be converted to this layer type.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiDense.MultiDense.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiDense.html#MultiDense.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiDense.MultiDense.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiDense.MultiDense.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiDense.html#MultiDense.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiDense.MultiDense.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiDense.MultiDense.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiDense.html#MultiDense.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiDense.MultiDense.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiDense.MultiDense.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiDense.html#MultiDense.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiDense.MultiDense.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiMaskedConv2D">
<span id="beyondml-tflow-layers-multimaskedconv2d-module"></span><h2>beyondml.tflow.layers.MultiMaskedConv2D module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiMaskedConv2D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiMaskedConv2D.</span></span><span class="sig-name descname"><span class="pre">MultiMaskedConv2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv2D.html#MultiMaskedConv2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Masked multitask 2-dimensional convolutional layer. This layer implements
multiple stacks of the convolutional architecture and implements masking consistent
with the BeyondML API to support developing sparse multitask models.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv2D.html#MultiMaskedConv2D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv2D.html#MultiMaskedConv2D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv2D.html#MultiMaskedConv2D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv2D.html#MultiMaskedConv2D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.kernel_size">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel_size</span></span><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.kernel_size" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.set_masks">
<span class="sig-name descname"><span class="pre">set_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_masks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv2D.html#MultiMaskedConv2D.set_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv2D.MultiMaskedConv2D.set_masks" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiMaskedConv3D">
<span id="beyondml-tflow-layers-multimaskedconv3d-module"></span><h2>beyondml.tflow.layers.MultiMaskedConv3D module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiMaskedConv3D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiMaskedConv3D.</span></span><span class="sig-name descname"><span class="pre">MultiMaskedConv3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv3D.html#MultiMaskedConv3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Masked multitask 3-dimensional convoluational layer. This layer implements
multiple stacks of the convolutional architecture and implements masking
consistent with the BeyondML API to support developing sparse multitask models.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv3D.html#MultiMaskedConv3D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv3D.html#MultiMaskedConv3D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv3D.html#MultiMaskedConv3D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv3D.html#MultiMaskedConv3D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.kernel_size">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel_size</span></span><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.kernel_size" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.set_masks">
<span class="sig-name descname"><span class="pre">set_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_masks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedConv3D.html#MultiMaskedConv3D.set_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedConv3D.MultiMaskedConv3D.set_masks" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiMaskedDense">
<span id="beyondml-tflow-layers-multimaskeddense-module"></span><h2>beyondml.tflow.layers.MultiMaskedDense module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiMaskedDense" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiMaskedDense.</span></span><span class="sig-name descname"><span class="pre">MultiMaskedDense</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedDense.html#MultiMaskedDense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Masked multitask fully connected layer. This layer implements multiple stacks
of the fully-connected architecture and implements masking with the BeyondML API
to support developing sparse multitask models.</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedDense.html#MultiMaskedDense.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedDense.html#MultiMaskedDense.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedDense.html#MultiMaskedDense.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedDense.html#MultiMaskedDense.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.set_masks">
<span class="sig-name descname"><span class="pre">set_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_masks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaskedDense.html#MultiMaskedDense.set_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaskedDense.MultiMaskedDense.set_masks" title="Permalink to this definition"></a></dt>
<dd><p>Set the masks for the layer</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>new_masks</strong> (<em>list</em><em> of </em><em>arrays</em><em> or </em><em>array-likes</em>) – The new masks to set for the layer</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiMaxPool2D">
<span id="beyondml-tflow-layers-multimaxpool2d-module"></span><h2>beyondml.tflow.layers.MultiMaxPool2D module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiMaxPool2D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiMaxPool2D.</span></span><span class="sig-name descname"><span class="pre">MultiMaxPool2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool2D.html#MultiMaxPool2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Multitask Max Pooling Layer. This layer implements the Max Pooling algorithm
across multiple inputs for developing multitask models</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool2D.html#MultiMaxPool2D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool2D.html#MultiMaxPool2D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool2D.html#MultiMaxPool2D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool2D.MultiMaxPool2D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultiMaxPool3D">
<span id="beyondml-tflow-layers-multimaxpool3d-module"></span><h2>beyondml.tflow.layers.MultiMaxPool3D module<a class="headerlink" href="#module-beyondml.tflow.layers.MultiMaxPool3D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultiMaxPool3D.</span></span><span class="sig-name descname"><span class="pre">MultiMaxPool3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool3D.html#MultiMaxPool3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Multitask 3D Max Pooling Layer. This layer implements the Max Pooling
algorithm across multiple inputs for developing multitask models</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool3D.html#MultiMaxPool3D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool3D.html#MultiMaxPool3D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultiMaxPool3D.html#MultiMaxPool3D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultiMaxPool3D.MultiMaxPool3D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.MultitaskNormalization">
<span id="beyondml-tflow-layers-multitasknormalization-module"></span><h2>beyondml.tflow.layers.MultitaskNormalization module<a class="headerlink" href="#module-beyondml.tflow.layers.MultitaskNormalization" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.MultitaskNormalization.</span></span><span class="sig-name descname"><span class="pre">MultitaskNormalization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultitaskNormalization.html#MultitaskNormalization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Multitask layer which normalizes all inputs to sum to 1</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultitaskNormalization.html#MultitaskNormalization.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.build" title="Permalink to this definition"></a></dt>
<dd><p>Creates the variables of the layer (for subclass implementers).</p>
<p>This is a method that implementers of subclasses of <cite>Layer</cite> or <cite>Model</cite>
can override if they need a state-creation step in-between
layer instantiation and layer call. It is invoked automatically before
the first execution of <cite>call()</cite>.</p>
<p>This is typically used to create the weights of <cite>Layer</cite> subclasses
(at the discretion of the subclass implementer).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>input_shape</strong> – Instance of <cite>TensorShape</cite>, or list of instances of
<cite>TensorShape</cite> if the layer expects a list of inputs
(one instance per input).</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultitaskNormalization.html#MultitaskNormalization.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultitaskNormalization.html#MultitaskNormalization.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/MultitaskNormalization.html#MultitaskNormalization.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.MultitaskNormalization.MultitaskNormalization.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SelectorLayer">
<span id="beyondml-tflow-layers-selectorlayer-module"></span><h2>beyondml.tflow.layers.SelectorLayer module<a class="headerlink" href="#module-beyondml.tflow.layers.SelectorLayer" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SelectorLayer.SelectorLayer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SelectorLayer.</span></span><span class="sig-name descname"><span class="pre">SelectorLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SelectorLayer.html#SelectorLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SelectorLayer.SelectorLayer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Layer which selects individual inputs</p>
<p>Example:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Create a model with two inputs and one SelectorLayer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span> <span class="o">=</span> <span class="n">mann</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">SelectorLayer</span><span class="p">(</span><span class="mi">1</span><span class="p">)([</span><span class="n">input_1</span><span class="p">,</span> <span class="n">input_2</span><span class="p">])</span> <span class="c1"># 1 here indicates to select the second input and return it</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">([</span><span class="n">input_1</span><span class="p">,</span> <span class="n">input_2</span><span class="p">],</span> <span class="n">selector</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Call the model</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data2</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">])</span>
<span class="go">array([[ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18.]], dtype=float32)</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SelectorLayer.SelectorLayer.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SelectorLayer.html#SelectorLayer.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SelectorLayer.SelectorLayer.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SelectorLayer.SelectorLayer.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SelectorLayer.html#SelectorLayer.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SelectorLayer.SelectorLayer.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SelectorLayer.SelectorLayer.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SelectorLayer.html#SelectorLayer.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SelectorLayer.SelectorLayer.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SelectorLayer.SelectorLayer.sel_index">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sel_index</span></span><a class="headerlink" href="#beyondml.tflow.layers.SelectorLayer.SelectorLayer.sel_index" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SparseConv2D">
<span id="beyondml-tflow-layers-sparseconv2d-module"></span><h2>beyondml.tflow.layers.SparseConv2D module<a class="headerlink" href="#module-beyondml.tflow.layers.SparseConv2D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv2D.SparseConv2D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SparseConv2D.</span></span><span class="sig-name descname"><span class="pre">SparseConv2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv2D.html#SparseConv2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv2D.SparseConv2D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Sparse implementation of the Convolutional layer. If used in a model,
must be saved and loaded via pickle</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv2D.SparseConv2D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv2D.html#SparseConv2D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv2D.SparseConv2D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv2D.SparseConv2D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv2D.html#SparseConv2D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv2D.SparseConv2D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv2D.SparseConv2D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv2D.html#SparseConv2D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv2D.SparseConv2D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv2D.SparseConv2D.from_layer">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_layer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv2D.html#SparseConv2D.from_layer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv2D.SparseConv2D.from_layer" title="Permalink to this definition"></a></dt>
<dd><p>Create a layer from an instance of another layer</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv2D.SparseConv2D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv2D.html#SparseConv2D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv2D.SparseConv2D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SparseConv3D">
<span id="beyondml-tflow-layers-sparseconv3d-module"></span><h2>beyondml.tflow.layers.SparseConv3D module<a class="headerlink" href="#module-beyondml.tflow.layers.SparseConv3D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv3D.SparseConv3D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SparseConv3D.</span></span><span class="sig-name descname"><span class="pre">SparseConv3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv3D.html#SparseConv3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv3D.SparseConv3D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Sparse implementation of the Convolutional layer. If used in a model,
must be saved and loaded via pickle</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv3D.SparseConv3D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv3D.html#SparseConv3D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv3D.SparseConv3D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv3D.SparseConv3D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv3D.html#SparseConv3D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv3D.SparseConv3D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv3D.SparseConv3D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv3D.html#SparseConv3D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv3D.SparseConv3D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv3D.SparseConv3D.from_layer">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_layer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv3D.html#SparseConv3D.from_layer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv3D.SparseConv3D.from_layer" title="Permalink to this definition"></a></dt>
<dd><p>Create a layer from an instance of another layer</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseConv3D.SparseConv3D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseConv3D.html#SparseConv3D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseConv3D.SparseConv3D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SparseDense">
<span id="beyondml-tflow-layers-sparsedense-module"></span><h2>beyondml.tflow.layers.SparseDense module<a class="headerlink" href="#module-beyondml.tflow.layers.SparseDense" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseDense.SparseDense">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SparseDense.</span></span><span class="sig-name descname"><span class="pre">SparseDense</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseDense.html#SparseDense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseDense.SparseDense" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Sparse implementation of the Dense layer. If used in a model, must be saved and loaded via pickle</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseDense.SparseDense.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseDense.html#SparseDense.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseDense.SparseDense.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseDense.SparseDense.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseDense.html#SparseDense.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseDense.SparseDense.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseDense.SparseDense.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseDense.html#SparseDense.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseDense.SparseDense.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseDense.SparseDense.from_layer">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_layer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseDense.html#SparseDense.from_layer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseDense.SparseDense.from_layer" title="Permalink to this definition"></a></dt>
<dd><p>Create a layer from an instance of another layer</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseDense.SparseDense.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseDense.html#SparseDense.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseDense.SparseDense.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SparseMultiConv2D">
<span id="beyondml-tflow-layers-sparsemulticonv2d-module"></span><h2>beyondml.tflow.layers.SparseMultiConv2D module<a class="headerlink" href="#module-beyondml.tflow.layers.SparseMultiConv2D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SparseMultiConv2D.</span></span><span class="sig-name descname"><span class="pre">SparseMultiConv2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv2D.html#SparseMultiConv2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Sparse implementation of the MultiConv layer. If used in a model, must be saved and loaded via pickle</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shapes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv2D.html#SparseMultiConv2D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv2D.html#SparseMultiConv2D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv2D.html#SparseMultiConv2D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.from_layer">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_layer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv2D.html#SparseMultiConv2D.from_layer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.from_layer" title="Permalink to this definition"></a></dt>
<dd><p>Create a layer from an instance of another layer</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv2D.html#SparseMultiConv2D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv2D.SparseMultiConv2D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SparseMultiConv3D">
<span id="beyondml-tflow-layers-sparsemulticonv3d-module"></span><h2>beyondml.tflow.layers.SparseMultiConv3D module<a class="headerlink" href="#module-beyondml.tflow.layers.SparseMultiConv3D" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SparseMultiConv3D.</span></span><span class="sig-name descname"><span class="pre">SparseMultiConv3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv3D.html#SparseMultiConv3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Sparse implementation of the MultiConv layer. If used in a model, must be saved and loaded via pickle</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv3D.html#SparseMultiConv3D.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv3D.html#SparseMultiConv3D.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv3D.html#SparseMultiConv3D.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.from_layer">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_layer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv3D.html#SparseMultiConv3D.from_layer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.from_layer" title="Permalink to this definition"></a></dt>
<dd><p>Create a layer from an instance of another layer</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiConv3D.html#SparseMultiConv3D.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiConv3D.SparseMultiConv3D.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SparseMultiDense">
<span id="beyondml-tflow-layers-sparsemultidense-module"></span><h2>beyondml.tflow.layers.SparseMultiDense module<a class="headerlink" href="#module-beyondml.tflow.layers.SparseMultiDense" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiDense.SparseMultiDense">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SparseMultiDense.</span></span><span class="sig-name descname"><span class="pre">SparseMultiDense</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiDense.html#SparseMultiDense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiDense.SparseMultiDense" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Sparse implementation of the MultiDense layer. If used in a model, must be saved and loaded via pickle</p>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.build">
<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiDense.html#SparseMultiDense.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.build" title="Permalink to this definition"></a></dt>
<dd><p>Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiDense.html#SparseMultiDense.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiDense.html#SparseMultiDense.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.from_layer">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_layer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiDense.html#SparseMultiDense.from_layer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.from_layer" title="Permalink to this definition"></a></dt>
<dd><p>Create a layer from an instance of another layer</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SparseMultiDense.html#SparseMultiDense.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SparseMultiDense.SparseMultiDense.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers.SumLayer">
<span id="beyondml-tflow-layers-sumlayer-module"></span><h2>beyondml.tflow.layers.SumLayer module<a class="headerlink" href="#module-beyondml.tflow.layers.SumLayer" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SumLayer.SumLayer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">beyondml.tflow.layers.SumLayer.</span></span><span class="sig-name descname"><span class="pre">SumLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SumLayer.html#SumLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SumLayer.SumLayer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Layer</span></code></p>
<p>Layer which adds all inputs together. All inputs must have compatible shapes</p>
<p>Example:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Create a model with just a SumLayer and two inputs</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">sum_layer</span> <span class="o">=</span> <span class="n">mann</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">SumLayer</span><span class="p">()([</span><span class="n">input_1</span><span class="p">,</span> <span class="n">input_2</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">([</span><span class="n">input_1</span><span class="p">,</span> <span class="n">input_2</span><span class="p">],</span> <span class="n">sum_layer</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Call the model</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="n">data</span><span class="p">,</span> <span class="n">data</span><span class="p">])</span>
<span class="go">array([[ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18.]], dtype=float32)</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SumLayer.SumLayer.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SumLayer.html#SumLayer.call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SumLayer.SumLayer.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives and is called upon inputs</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>TensorFlow Tensor</em><em> or </em><em>Tensor-like</em>) – The inputs to the layer</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>outputs</strong> – The outputs of the layer’s logic</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>TensorFlow Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SumLayer.SumLayer.from_config">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SumLayer.html#SumLayer.from_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SumLayer.SumLayer.from_config" title="Permalink to this definition"></a></dt>
<dd><p>Creates a layer from its config.</p>
<p>This method is the reverse of <cite>get_config</cite>,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by <cite>set_weights</cite>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>config</strong> – A Python dictionary, typically the
output of get_config.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A layer instance.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="beyondml.tflow.layers.SumLayer.SumLayer.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/beyondml/tflow/layers/SumLayer.html#SumLayer.get_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#beyondml.tflow.layers.SumLayer.SumLayer.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-beyondml.tflow.layers">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-beyondml.tflow.layers" title="Permalink to this heading"></a></h2>
<p>Custom layers to use when building MANN models</p>
</section>
</section>


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